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Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add github/awesome-copilot --skill arize-experiment -a cursor -y
Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.
Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.
SPACE— All--spaceflags and theARIZE_SPACEenv var accept a space name (e.g.,my-workspace) or a base64 space ID (e.g.,U3BhY2U6...). Find yours withax spaces list.
correctness, relevance), with optional label, score, and explanationThe typical flow: export a dataset → process each example → collect outputs and evaluations → create an experiment with the runs.
Proceed directly with the task — run the ax command you need. Do NOT check versions, env vars, or profiles upfront.
If an ax command fails, troubleshoot based on the error:
command not found or version error → see references/ax-setup.md401 Unauthorized / missing API key → run ax profiles show to inspect the current profile. If the profile is missing or the API key is wrong, follow references/ax-profiles.md to create/update it. If the user doesn't have their key, direct them to https://app.arize.com/admin > API Keysax spaces list to pick by name, or ask the userax projects list -o json --limit 100 and present as selectable options.env files or search the filesystem for credentials. Use ax profiles for Arize credentials and ax ai-integrations for LLM provider keys. If credentials are not available through these channels, ask the user.ax experiments listBrowse experiments, optionally filtered by dataset. Output goes to stdout.
ax experiments list
ax experiments list --dataset DATASET_NAME --space SPACE --limit 20 # DATASET_NAME: name or ID (name preferred)
ax experiments list --cursor CURSOR_TOKEN
ax experiments list -o json
| Flag | Type | Default | Description |
|---|---|---|---|
--dataset | string | none | Filter by dataset |
--limit, -l | int | 15 | Max results (1-100) |
--cursor | string | none | Pagination cursor from previous response |
-o, --output | string | table | Output format: table, json, csv, parquet, or file path |
-p, --profile | string | default | Configuration profile |
ax experiments getQuick metadata lookup -- returns experiment name, linked dataset/version, and timestamps.
ax experiments get NAME_OR_ID
ax experiments get NAME_OR_ID -o json
ax experiments get NAME_OR_ID --dataset DATASET_NAME --space SPACE # required when using experiment name instead of ID
| Flag | Type | Default | Description |
|---|---|---|---|
NAME_OR_ID | string | required | Experiment name or ID (positional) |
--dataset | string | none | Dataset name or ID (required if using experiment name instead of ID) |
--space | string | none | Space name or ID (required if using dataset name instead of ID) |
-o, --output | string | table | Output format |
-p, --profile | string | default | Configuration profile |
| Field | Type | Description |
|---|---|---|
id | string | Experiment ID |
name | string | Experiment name |
dataset_id | string | Linked dataset ID |
dataset_version_id | string | Specific dataset version used |
experiment_traces_project_id | string | Project where experiment traces are stored |
created_at | datetime | When the experiment was created |
updated_at | datetime | Last modification time |
ax experiments exportDownload all runs to a file. By default uses the REST API; pass --all to use Arrow Flight for bulk transfer.
# EXPERIMENT_NAME, DATASET_NAME: name or ID (name preferred)
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE
# -> experiment_abc123_20260305_141500/runs.json
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --all
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --output-dir ./results
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | jq '.[0]'
| Flag | Type | Default | Description |
|---|---|---|---|
NAME_OR_ID | string | required | Experiment name or ID (positional) |
--dataset | string | none | Dataset name or ID (required if using experiment name instead of ID) |
--space | string | none | Space name or ID (required if using dataset name instead of ID) |
--all | bool | false | Use Arrow Flight for bulk export (see below) |
--output-dir | string | . | Output directory |
--stdout | bool | false | Print JSON to stdout instead of file |
-p, --profile | string | default | Configuration profile |
--all)--all): Required for experiments with more than 500 runs. Uses gRPC+TLS on a separate host/port (flight.arize.com:443) which some corporate networks may block.Agent auto-escalation rule: If a REST export returns exactly 500 runs, the result is likely truncated. Re-run with --all to get the full dataset.
Output is a JSON array of run objects:
[
{
"id": "run_001",
"example_id": "ex_001",
"output": "The answer is 4.",
"evaluations": {
"correctness": { "label": "correct", "score": 1.0 },
"relevance": { "score": 0.95, "explanation": "Directly answers the question" }
},
"metadata": { "model": "gpt-4o", "latency_ms": 1234 }
}
]
ax experiments createCreate a new experiment with runs from a data file.
ax experiments create --name "gpt-4o-baseline" --dataset DATASET_NAME --space SPACE --file runs.json
ax experiments create --name "claude-test" --dataset DATASET_NAME --space SPACE --file runs.csv
| Flag | Type | Required | Description |
|---|---|---|---|
--name, -n | string | yes | Experiment name |
--dataset | string | yes | Dataset to run the experiment against |
--space, -s | string | no | Space name or ID (required if using dataset name instead of ID) |
--file, -f | path | yes | Data file with runs: CSV, JSON, JSONL, or Parquet |
-o, --output | string | no | Output format |
-p, --profile | string | no | Configuration profile |
Use --file - to pipe data directly — no temp file needed:
echo '[{"example_id": "ex_001", "output": "Paris"}]' | ax experiments create --name "my-experiment" --dataset DATASET_NAME --space SPACE --file -
# Or with a heredoc
ax experiments create --name "my-experiment" --dataset DATASET_NAME --space SPACE --file - << 'EOF'
[{"example_id": "ex_001", "output": "Paris"}]
EOF
| Column | Type | Required | Description |
|---|---|---|---|
example_id | string | yes | ID of the dataset example this run corresponds to |
output | string | yes | The model/system output for this example |
Additional columns are passed through as additionalProperties on the run.
ax experiments deleteax experiments delete NAME_OR_ID
ax experiments delete NAME_OR_ID --dataset DATASET_NAME --space SPACE # required when using experiment name instead of ID
ax experiments delete NAME_OR_ID --force # skip confirmation prompt
| Flag | Type | Default | Description |
|---|---|---|---|
NAME_OR_ID | string | required | Experiment name or ID (positional) |
--dataset | string | none | Dataset name or ID (required if using experiment name instead of ID) |
--space | string | none | Space name or ID (required if using dataset name instead of ID) |
--force, -f | bool | false | Skip confirmation prompt |
-p, --profile | string | default | Configuration profile |
Each run corresponds to one dataset example:
{
"example_id": "required -- links to dataset example",
"output": "required -- the model/system output for this example",
"evaluations": {
"metric_name": {
"label": "optional string label (e.g., 'correct', 'incorrect')",
"score": "optional numeric score (e.g., 0.95)",
"explanation": "optional freeform text"
}
},
"metadata": {
"model": "gpt-4o",
"temperature": 0.7,
"latency_ms": 1234
}
}
| Field | Type | Required | Description |
|---|---|---|---|
label | string | no | Categorical classification (e.g., correct, incorrect, partial) |
score | number | no | Numeric quality score (e.g., 0.0 - 1.0) |
explanation | string | no | Freeform reasoning for the evaluation |
At least one of label, score, or explanation should be present per evaluation.
Find or create a dataset:
ax datasets list --space SPACE
ax datasets export DATASET_NAME --space SPACE --stdout | jq 'length'
Export the dataset examples:
ax datasets export DATASET_NAME --space SPACE
Call the real model API for each example and collect outputs. Use ax datasets export --stdout to pipe examples directly into an inference script:
ax datasets export DATASET_NAME --space SPACE --stdout | python3 infer.py > runs.json
Write infer.py to read examples from stdin, call the target model, and write runs JSON to stdout. The script below is a template — first inspect the exported dataset JSON to find the correct input field name, then uncomment the provider block the user wants:
import json, sys, time
examples = json.load(sys.stdin)
runs = []
for ex in examples:
# Inspect the exported JSON to find the right field (e.g. "input", "question", "prompt")
user_input = ex.get("input") or ex.get("question") or ex.get("prompt") or str(ex)
start = time.time()
# === CALL THE REAL MODEL API HERE — never fabricate or simulate ===
# Uncomment and adapt the provider block the user requested:
#
# OpenAI (pip install openai — uses OPENAI_API_KEY env var):
# from openai import OpenAI
# resp = OpenAI().chat.completions.create(
# model="gpt-4o",
# messages=[{"role": "user", "content": user_input}]
# )
# output_text = resp.choices[0].message.content
#
# Anthropic (pip install anthropic — uses ANTHROPIC_API_KEY env var):
# import anthropic
# resp = anthropic.Anthropic().messages.create(
# model="claude-sonnet-4-6", max_tokens=1024,
# messages=[{"role": "user", "content": user_input}]
# )
# output_text = resp.content[0].text
#
# Google Gemini (pip install google-genai — uses GOOGLE_API_KEY env var):
# from google import genai
# resp = genai.Client().models.generate_content(
# model="gemini-2.5-pro", contents=user_input
# )
# output_text = resp.text
#
# Custom / OpenAI-compatible proxy (pip install openai — uses CUSTOM_BASE_URL + CUSTOM_API_KEY env vars):
# Use this for Azure OpenAI, NVIDIA NIM, local Ollama, or any OpenAI-compatible endpoint,
# including a test integration proxy. Matches the `custom` provider in `ax ai-integrations create`.
# import os
# from openai import OpenAI
# resp = OpenAI(
# base_url=os.environ["CUSTOM_BASE_URL"], # e.g. https://my-proxy.example.com/v1
# api_key=os.environ.get("CUSTOM_API_KEY", "none"),
# ).chat.completions.create(
# model=os.environ.get("CUSTOM_MODEL", "default"),
# messages=[{"role": "user", "content": user_input}]
# )
# output_text = resp.choices[0].message.content
latency_ms = round((time.time() - start) * 1000)
runs.append({
"example_id": ex["id"],
"output": output_text,
"metadata": {"model": "MODEL_NAME", "latency_ms": latency_ms}
})
print(f" {ex['id']}: {latency_ms}ms", file=sys.stderr)
json.dump(runs, sys.stdout, indent=2)
Before running: install the provider SDK (pip install openai / anthropic / google-genai) and ensure the API key is set as an environment variable in your shell. If you cannot access the API, stop and tell the user what is needed.
Verify the runs file:
python3 -c "import json; runs=json.load(open('runs.json')); print(f'{len(runs)} runs'); print(json.dumps(runs[0], indent=2))"
Each run must have example_id and output. Optional fields: evaluations, metadata.
Create the experiment:
ax experiments create --name "gpt-4o-baseline" --dataset DATASET_NAME --space SPACE --file runs.json
Verify: ax experiments get "gpt-4o-baseline" --dataset DATASET_NAME --space SPACE
ax experiments export "experiment-a" --dataset DATASET_NAME --space SPACE --stdout > a.json
ax experiments export "experiment-b" --dataset DATASET_NAME --space SPACE --stdout > b.json
example_id:
# Average correctness score for experiment A
jq '[.[] | .evaluations.correctness.score] | add / length' a.json
# Same for experiment B
jq '[.[] | .evaluations.correctness.score] | add / length' b.json
jq -s '.[0] as $a | .[1][] | . as $run |
{
example_id: $run.example_id,
b_score: $run.evaluations.correctness.score,
a_score: ($a[] | select(.example_id == $run.example_id) | .evaluations.correctness.score)
}' a.json b.json
# Count by label for experiment A
jq '[.[] | .evaluations.correctness.label] | group_by(.) | map({label: .[0], count: length})' a.json
jq -s '
[.[0][] | select(.evaluations.correctness.label == "correct")] as $passed_a |
[.[1][] | select(.evaluations.correctness.label != "correct") |
select(.example_id as $id | $passed_a | any(.example_id == $id))
]
' a.json b.json
Statistical significance note: Score comparisons are most reliable with ≥ 30 examples per evaluator. With fewer examples, treat the delta as directional only — a 5% difference on n=10 may be noise. Report sample size alongside scores: jq 'length' a.json.
ax experiments list --dataset DATASET_NAME --space SPACE -- find experimentsax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE -- download to filejq '.[] | {example_id, score: .evaluations.correctness.score}' experiment_*/runs.json# Count runs
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | jq 'length'
# Extract all outputs
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | jq '.[].output'
# Get runs with low scores
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | jq '[.[] | select(.evaluations.correctness.score < 0.5)]'
# Convert to CSV
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | jq -r '.[] | [.example_id, .output, .evaluations.correctness.score] | @csv'
arize-dataset firstarize-prompt-optimizationarize-tracearize-link| Problem | Solution |
|---|---|
ax: command not found | See references/ax-setup.md |
401 Unauthorized | API key is wrong, expired, or doesn't have access to this space. Fix the profile using references/ax-profiles.md. |
No profile found | No profile is configured. See references/ax-profiles.md to create one. |
Experiment not found | Verify experiment name with ax experiments list --space SPACE |
Invalid runs file | Each run must have example_id and output fields |
example_id mismatch | Ensure example_id values match IDs from the dataset (export dataset to verify) |
No runs found | Export returned empty -- verify experiment has runs via ax experiments get |
Dataset not found | The linked dataset may have been deleted; check with ax datasets list |
See references/ax-profiles.md § Save Credentials for Future Use.